PREDICTION OF PATHOLOGICAL STAGE IN PROSTATE CANCER PATIENTS BY PROSTATE MRI: ARTIFICIAL NEURAL NETWORKS METHODS

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PREDICTION OF PATHOLOGICAL STAGE IN PROSTATE CANCER PATIENTS BY PROSTATE MRI: ARTIFICIAL NEURAL NETWORKS METHODS

Journal Title: Nature & Science - Year 2020, Vol 2, Issue 2

Abstract

Prostate cancer is a disease that is most common in males and causes death in the second frequency in the world. If prostate cancer is diagnosed in the early stages, surgery can be performed and the disease can be cured. The aim of this study is to design an expert system to catch prostate cancer as early as possible with the chance of surgical treatment by being diagnosed in the limited phase of the organ. The most accurate diagnosis is to use risk factors such as Age, PSA (prostate Specific antigen), Clinical Stage, Tumor Size, Prostate Size and ISUP (International Society of Urological Pathology). In other words, it is aimed to biopsy the minimum number of patients and to diagnose the largest number of cancers. For better detection both sets of characteristics are used in our research. In this study, as a diagnostic model, we use a system based on multiple-layer (deep) feed-forward neural networks. The networks are trained with Differential Evolution training algorithm using in parallel a pair of data sets (training and validation sets) to avoid overfitting and improve model’s generalization ability (performance on untrained data). The applied DE algorithm has allowed avoiding local minima of error function during the training. A third data set is used for testing trained network performance. According to the obtained results, this method demonstrated better results than other existing approaches.

Authors and Affiliations

Emin Mammadov, Elcin Nizami Huseyn

Keywords

Key words: Prostate Cancer Artificial Neural Network Differential Evolution Optimization Computational Intelligence Methods

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EP ID EP759472
DOI 10.36719/2707-1146/03/25-29
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